Introduction / Context:
The statement forecasts widespread farmer distress if there is no rain this month. The logic rests on rainfall being a key input and on most farmers depending on it rather than on assured irrigation.
Given Data / Assumptions:
- Condition: No rain this month.
- Outcome predicted: Most farmers will be in trouble.
- Assumption I: Rainfall timing is essential for crop cycles.
- Assumption II: A majority of farmers rely on rain-fed agriculture.
Concept / Approach:
- A conditional forecast requires that the condition (rain) is causally significant.
- The aggregate prediction (“most farmers”) requires that reliance on rain is widespread among farmers.
Step-by-Step Solution:
Assumption I is necessary: without the importance of timely rain, the absence of rain would not broadly harm farming.Assumption II is necessary: if most farmers had irrigation or other substitutes, “most” would not be in trouble.
Verification / Alternative check:
Remove I: The causal link fails. Remove II: The quantifier “most” is unsupported. With both present, the prediction is coherent.
Why Other Options Are Wrong:
Only I or Only II under-supports either the causal mechanism or the breadth of impact. Either / Neither miss the dual necessity.
Common Pitfalls:
Ignoring the quantifier “most,” which demands an assumption about widespread dependence, not just isolated cases.
Final Answer:
Both I and II are implicit
Discussion & Comments